SCISIPBIO: Maximizing the Value of Sex-Inclusive Research Policies through Analytical Rigor

历史上，临床前研究不成比例地集中在男性身上。这种偏见限制了科学的进步，以促进国家的健康和福利。2016年，为了鼓励男性和女性参与研究，美国国立卫生研究院实施了一项政策，要求在所有资助的研究中将性别视为生物变量（SABV）。虽然在研究中纳入女性的情况有所改善，但基于性别的数据往往没有采用严格的方法进行分析。我们对SABV依从性研究的初步分析表明，当作者报告性别特异性效应时，他们仅在29%的时间内对此类效应进行了统计学检验。相反，性别特异性发现的主张通常仅依赖于断言。我们以前的研究结果清楚地表明，SABV的实施并不严格，SABV合规研究的同行评审过程需要改进。性别差异的虚假声明可能导致资源浪费在不存在的“差异”上，并导致获得有效治疗的不平等。同样，不实的无差别声明可能导致错失为所有性别的人提供有效保健的机会。SABV的预期目标，即提高可重复性和促进基于性别的精确医学，没有得到满足。在本项目中，我们将确定不适当的分析方法对准确报告性别特异性效应的影响，确定影响基于性别的数据分析方法决策的因素，并传播设计和评估考虑SABV的研究的工具。我们将使用混合方法，包括分析已发表的期刊文章，采访科学家和推广。我们将对生物医学文献进行大规模分析，以显示基于性别的数据的不适当统计方法如何影响SABV的严格实施。其次，我们将对其中一些出版物的作者进行半结构化访谈，以确定促进最佳实践的因素。总之，这些方法将为制定适当分析基于性别的数据的工作流程提供信息。第三，我们将公开我们优化的工作流程，并广泛传播。此外，我们将与期刊编辑合作开发一个简单的工具，供评审人员使用，以评估性别特异性研究结果的有效性。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响评审标准进行评估来支持。

Historically, preclinical research has focused disproportionately on males. This bias has limited the progress of science to advance national health and welfare. In 2016, to encourage inclusion of both males and females in research, the National Institutes of Health implemented a policy requiring the consideration of sex as a biological variable (SABV) in all funded studies. Although inclusion of females in research has improved, sex-based data are often not being analyzed using rigorous approaches. Our preliminary analysis of SABV-compliant studies showed that when authors report sex-specific effects, they tested statistically for such effects only 29% of the time. Instead, claims of sex-specific findings typically rest on assertion alone. Our previous findings indicate clearly that the implementation of SABV has not been rigorous and that the process of peer review of SABV-compliant research needs to improve. False claims of sex differences can lead to the wasting of resources on ‘differences’ that do not exist, and to inequities in access to effective treatments. Similarly, false claims of non-differences can lead to missed opportunities to provide effective health care for people of all sexes and genders. The intended goals of SABV, namely to enhance reproducibility and to facilitate sex-based precision medicine, are not being met. In this project we will determine the impact of inappropriate analytical approaches on the accurate reporting of sex-specific effects, identify factors that inform decisions about analytical approaches to sex-based data, and disseminate tools for designing and evaluating studies that consider SABV. We will use a mixed method approach that includes analyses of published journal articles, interviews with scientists, and outreach. We will conduct a large-scale analysis of the biomedical literature to show how inappropriate statistical approaches to sex-based data are impacting the rigorous implementation of SABV. Second, we will conduct semi-structured interviews with authors of some of those publications to identify the factors that facilitate best practices. Together, these methods will inform the development of a workflow for appropriate analysis of sex-based data. Third, we will make our optimized workflow publicly available and disseminate it widely. In addition, we will partner with journal editors to develop a simple tool that can be used by reviewers to evaluate the validity of sex-specific findings.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.